Historical heritage is demanding robust pipelines for obtaining Heritage Building Information Modeling models that are fully interoperable and rich in their informative content. The definition of efficient Scan-to-BIM workflows represent a very important step toward a more efficient management of the historical real estate, as creating structured three-dimensional (3D) models from point clouds is complex and time-consuming. In this scenario, semantic segmentation of 3D Point Clouds is gaining more and more attention, since it might help to automatically recognize historical architectural elements. The way paved by recent Deep Learning approaches proved to provide reliable and affordable degrees of automation in other contexts, as road scenes understanding. However, semantic segmentation is particularly challenging in historical and classical architecture, due to the shapes complexity and the limited repeatability of elements across different buildings, which makes it difficult to define common patterns within the same class of elements. Furthermore, as Deep Learning models requires a considerably large amount of annotated data to be trained and tuned to properly handle unseen scenes, the lack of (big) publicly available annotated point clouds in the historical building domain is a huge problem, which in fact blocks the research in this direction. However, creating a critical mass of annotated point clouds by manual annotation is very time-consuming and impractical. To tackle this issue, in this work we explore the idea of leveraging synthetic point cloud data to train Deep Learning models to perform semantic segmentation of point clouds obtained via Terrestrial Laser Scanning. The aim is to provide a first assessment of the use of synthetic data to drive Deep Learning--based semantic segmentation in the context of historical buildings. To achieve this purpose, we present an improved version of the Dynamic Graph CNN (DGCNN) named RadDGCNN. The main improvement consists on exploiting the radius distance. In our experiments, we evaluate the trained models on synthetic dataset (publicly available) about two different historical buildings: the Ducal Palace in Urbino, Italy, and Palazzo Ferretti in Ancona, Italy. RadDGCNN yields good results, demonstrating improved segmentation performances on the TLS real datasets.

中文翻译：

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从合成点云数据中学习历史建筑语义分割

历史遗产需要强大的管道来获取完全可互操作且信息丰富的遗产建筑信息模型模型。高效的 Scan-to-BIM 工作流程的定义代表了朝着更有效地管理历史不动产迈出了非常重要的一步，因为从点云创建结构化的三维 (3D) 模型既复杂又耗时。在这种情况下，3D 点云的语义分割越来越受到关注，因为它可能有助于自动识别历史建筑元素。事实证明，最近的深度学习方法铺平的道路在其他情况下提供了可靠且负担得起的自动化程度，例如道路场景理解。然而，语义分割在历史和古典建筑中尤其具有挑战性，因为不同建筑中元素的形状复杂性和重复性有限，这使得在同一类元素中定义共同模式变得困难。此外，由于深度学习模型需要训练和调整大量带注释的数据以正确处理看不见的场景，因此在历史建筑领域缺乏（大）公开可用的带注释的点云是一个巨大的问题，这实际上阻碍了这个方向的研究。但是，通过手动注释创建临界质量的注释点云非常耗时且不切实际。为了解决这个问题，在这项工作中，我们探索了利用合成点云数据来训练深度学习模型以对通过地面激光扫描获得的点云进行语义分割的想法。目的是首次评估使用合成数据来推动历史建筑背景下基于深度学习的语义分割。为了达到这个目的，我们提出了一个名为 RadDGCNN 的动态图 CNN (DGCNN) 的改进版本。主要改进在于利用半径距离。在我们的实验中，我们在合成数据集（公开可用）上评估了关于两个不同历史建筑的训练模型：意大利乌尔比诺的公爵宫和意大利安科纳的费雷蒂宫。RadDGCNN 产生了很好的结果，证明了在 TLS 真实数据集上的分割性能有所提高。